climpred.prediction.compute_hindcast(hind, reference, metric='pearson_r', comparison='e2r', dim='init', max_dof=False, add_attrs=True, **metric_kwargs)[source]

Compute a predictability skill score against a reference

  • hind (xarray object) – Expected to follow package conventions: * init : dim of initialization dates * lead : dim of lead time from those initializations Additional dims can be member, lat, lon, depth, …
  • reference (xarray object) – reference output/data over same time period.
  • metric (str) – Metric used in comparing the decadal prediction ensemble with the reference (see climpred.utils.get_metric_function() and Metrics).
  • comparison (str) –

    How to compare the decadal prediction ensemble to the reference:

    • e2r : ensemble mean to reference (Default)
    • m2r : each member to the reference

    (see Comparisons)

  • dim (str or list) – dimension to apply metric over. default: ‘init’
  • max_dof (bool) –

    If True, maximize the degrees of freedom by slicing hind and reference to a common time frame at each lead.

    If False (default), then slice to a common time frame prior to computing metric. This philosophy follows the thought that each lead should be based on the same set of initializations.

  • add_attrs (bool) – write climpred compute args to attrs. default: True
  • metric_kwargs (**) – additional keywords to be passed to metric (see the arguments required for a given metric in Metrics).

Predictability with main dimension lag without dimension dim

Return type:

skill (xarray object)